knitr::opts_chunk$set(echo = TRUE)
library(tidyverse)
library(plm)
library(splm)
library(sp)
library(spdep)
library(mgcv)
library(lmtest)
library(psych)
library(ppcor)
library(corrplot)
library(lavaan)
library(lavaanPlot)
library(DiagrammeRsvg)
library(car)
df = read.csv("Data/ExtendedDataset.csv")
Ez a medián a CSOK-os években
median(df$CSOK[df$EV > "2015"])
## [1] 74
df$JARAS_NEV = as.factor(df$JARAS_NEV)
df$EV = as.factor(df$EV)
A mediánnál elválasztva:
cuts = c(-Inf, 1, 75, Inf)
labs = c("nincs", "alacsony", "magas")
df$CSOKTREND = cut(df$CSOK, breaks = cuts, labels = labs, include.lowest = T)
rm(cuts, labs)
gammodel = gam(LAKAS ~ s(X,Y, by = EV, k = 8), family = "gaussian", data = df, method = "REML")
predicted = predict(gammodel, df)
df$LAKAS_PRED = df$LAKAS
for (i in 1:nrow(df)) {
if(is.na(df$LAKAS[i])){
if(predicted[i] > 0){
df$LAKAS_PRED[i] = predicted[i]
}else{
df$LAKAS_PRED[i] = 0
}
}
}
sapply(df[,20:37], function(x) sum(is.na(x)))
## HO_SZAM HGYO_SZAM HO_APOLO_SZAM
## 0 49 0
## HO_SZOLG_SZAM HGYO_SZOLG_SZAM FHO_SZOLG_SZAM
## 0 41 40
## FGYHO_SZOLG_SZAM HO_HELY_SZOLG_SZAM HGYO_HELY_SZOLG_SZAM
## 51 446 1159
## HO_TMED_SZAM HGYO_TMED_SZAMA HO_FORG_OSSZ
## 1575 1582 0
## HGYO_FORG_OSSZ HGYO_FORG_RB HGYO_FORG_RK
## 41 41 57
## HO_FORG_RB HO_FORG_RK VEDONO
## 0 0 0
gammodel = gam(FGYHO_SZOLG_SZAM ~ s(X,Y, by = EV, k = 8), family = "gaussian", data = df, method = "REML")
predicted = predict(gammodel, df)
for (i in 1:nrow(df)) {
if(is.na(df$FGYHO_SZOLG_SZAM[i])){
if(predicted[i] > 0){
df$FGYHO_SZOLG_SZAM[i] = predicted[i]
}else{
df$FGYHO_SZOLG_SZAM[i] = 0
}
}
}
gammodel = gam(HGYO_SZAM ~ s(X,Y, by = EV, k = 8), family = "gaussian", data = df, method = "REML")
predicted = predict(gammodel, df)
for (i in 1:nrow(df)) {
if(is.na(df$HGYO_SZAM[i])){
if(predicted[i] > 0){
df$HGYO_SZAM[i] = predicted[i]
}else{
df$HGYO_SZAM[i] = 0
}
}
}
rm(i, predicted, gammodel)
df2012 = filter(df, EV == 2012)
df2013 = filter(df, EV == 2013)
df2014 = filter(df, EV == 2014)
df2015 = filter(df, EV == 2015)
df2016 = filter(df, EV == 2016)
df2017 = filter(df, EV == 2017)
df2018 = filter(df, EV == 2018)
df2019 = filter(df, EV == 2019)
df2020 = filter(df, EV == 2020)
df2021 = filter(df, EV == 2021)
listw32 = nb2listw(dnearneigh(df2016[,c(9,10)], 0, 32, longlat = T))
listw36 = nb2listw(dnearneigh(df2016[,c(9,10)], 0, 36, longlat = T))
listw40 = nb2listw(dnearneigh(df2016[,c(9,10)], 0, 40, longlat = T))
# Moran, Geary 2016
moran(df2016$LAKAS_PRED, listw32, 175, 174, zero.policy = NULL, NAOK = F)
moran(df2016$LAKAS_PRED, listw36, 175, 174, zero.policy = NULL, NAOK = F)
moran(df2016$LAKAS_PRED, listw40, 175, 174, zero.policy = NULL, NAOK = F)
geary(df2016$LAKAS_PRED, listw32, 175, 174, Szero(listw32))
geary(df2016$LAKAS_PRED, listw36, 175, 174, Szero(listw36))
geary(df2016$LAKAS_PRED, listw40, 175, 174, Szero(listw40))
# Moran, Geary 2017
moran(df2017$LAKAS_PRED, listw32, 175, 174, zero.policy = NULL, NAOK = F)
moran(df2017$LAKAS_PRED, listw36, 175, 174, zero.policy = NULL, NAOK = F)
moran(df2017$LAKAS_PRED, listw40, 175, 174, zero.policy = NULL, NAOK = F)
geary(df2017$LAKAS_PRED, listw32, 175, 174, Szero(listw32))
geary(df2017$LAKAS_PRED, listw36, 175, 174, Szero(listw36))
geary(df2017$LAKAS_PRED, listw40, 175, 174, Szero(listw40))
# Moran, Geary 2018
moran(df2018$LAKAS_PRED, listw32, 175, 174, zero.policy = NULL, NAOK = F)
moran(df2018$LAKAS_PRED, listw36, 175, 174, zero.policy = NULL, NAOK = F)
moran(df2018$LAKAS_PRED, listw40, 175, 174, zero.policy = NULL, NAOK = F)
geary(df2018$LAKAS_PRED, listw32, 175, 174, Szero(listw32))
geary(df2018$LAKAS_PRED, listw36, 175, 174, Szero(listw36))
geary(df2018$LAKAS_PRED, listw40, 175, 174, Szero(listw40))
# Moran, Geary 2019
moran(df2019$LAKAS_PRED, listw32, 175, 174, zero.policy = NULL, NAOK = F)
moran(df2019$LAKAS_PRED, listw36, 175, 174, zero.policy = NULL, NAOK = F)
moran(df2019$LAKAS_PRED, listw40, 175, 174, zero.policy = NULL, NAOK = F)
geary(df2019$LAKAS_PRED, listw32, 175, 174, Szero(listw32))
geary(df2019$LAKAS_PRED, listw36, 175, 174, Szero(listw36))
geary(df2019$LAKAS_PRED, listw40, 175, 174, Szero(listw40))
# Moran, Geary 2020
moran(df2020$LAKAS_PRED, listw32, 175, 174, zero.policy = NULL, NAOK = F)
moran(df2020$LAKAS_PRED, listw36, 175, 174, zero.policy = NULL, NAOK = F)
moran(df2020$LAKAS_PRED, listw40, 175, 174, zero.policy = NULL, NAOK = F)
geary(df2020$LAKAS_PRED, listw32, 175, 174, Szero(listw32))
geary(df2020$LAKAS_PRED, listw36, 175, 174, Szero(listw36))
geary(df2020$LAKAS_PRED, listw40, 175, 174, Szero(listw40))
describe(df2012$LAKAS)
describe(df2013$LAKAS)
describe(df2014$LAKAS)
describe(df2015$LAKAS)
describe(df2016$LAKAS)
describe(df2017$LAKAS)
describe(df2018$LAKAS)
describe(df2019$LAKAS)
describe(df2020$LAKAS)
describe(df2021$LAKAS)
psych::describe(df2021$ATLAGAR)
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 175 15.74 11.78 11.69 13.54 7.02 3 68.51 65.51 2.01 4.58 0.89
cor(df[,c(4:8,14:19)], use = "complete.obs")
## LAKAS SZJA MUNKA BERUHAZAS CSOK
## LAKAS 1.00000000 0.4524761 0.37681306 0.21970624 0.02798068
## SZJA 0.45247607 1.0000000 0.76176321 0.65480917 0.15495230
## MUNKA 0.37681306 0.7617632 1.00000000 0.55171493 0.19346516
## BERUHAZAS 0.21970624 0.6548092 0.55171493 1.00000000 0.09673088
## CSOK 0.02798068 0.1549523 0.19346516 0.09673088 1.00000000
## BUNOZES -0.05715594 -0.2520321 -0.25982415 -0.21406135 -0.08827811
## SERTETT -0.14519939 -0.4786846 -0.51769037 -0.31142199 -0.19991632
## PEDAGOGUS 0.23637629 0.1830092 0.07755174 0.16020173 -0.07238756
## ORVOS -0.33984858 -0.3451780 -0.30159493 -0.10513064 0.17688275
## GYORVOS -0.08994859 -0.1652809 -0.12108496 -0.05896689 0.02580778
## ATLAGAR 0.69680891 0.7430468 0.55728901 0.38713991 0.01480768
## BUNOZES SERTETT PEDAGOGUS ORVOS GYORVOS
## LAKAS -0.05715594 -0.1451994 0.23637629 -0.33984858 -0.08994859
## SZJA -0.25203211 -0.4786846 0.18300918 -0.34517798 -0.16528087
## MUNKA -0.25982415 -0.5176904 0.07755174 -0.30159493 -0.12108496
## BERUHAZAS -0.21406135 -0.3114220 0.16020173 -0.10513064 -0.05896689
## CSOK -0.08827811 -0.1999163 -0.07238756 0.17688275 0.02580778
## BUNOZES 1.00000000 0.5331466 0.03582450 0.05974800 0.06137758
## SERTETT 0.53314655 1.0000000 0.10062095 0.17288650 0.14297398
## PEDAGOGUS 0.03582450 0.1006210 1.00000000 0.06395328 0.07404506
## ORVOS 0.05974800 0.1728865 0.06395328 1.00000000 0.14647215
## GYORVOS 0.06137758 0.1429740 0.07404506 0.14647215 1.00000000
## ATLAGAR -0.10765804 -0.2622687 0.27325799 -0.45604068 -0.15125215
## ATLAGAR
## LAKAS 0.69680891
## SZJA 0.74304678
## MUNKA 0.55728901
## BERUHAZAS 0.38713991
## CSOK 0.01480768
## BUNOZES -0.10765804
## SERTETT -0.26226871
## PEDAGOGUS 0.27325799
## ORVOS -0.45604068
## GYORVOS -0.15125215
## ATLAGAR 1.00000000
corrplot(cor(df[,c(4:8,14:37)], use = "complete.obs"), method = "color")
corrplot(cor(df[,c(4:8,14:37)], use = "complete.obs"), method = "color", addCoef.col = "darkolivegreen")
pcor(na.omit(df[,c(4:8,38:41)]))$estimate
## Error in `[.data.frame`(df, , c(4:8, 38:41)): undefined columns selected
corrplot(pcor(na.omit(df[,c(4:8,14:16,19,38:40)]))$estimate, method = "color")
## Error in `[.data.frame`(df, , c(4:8, 14:16, 19, 38:40)): undefined columns selected
itt a nagy kép:
knitr::include_graphics("tdk_files/figure-gfm/parcialis corrplot-1.png")
### Főkomponensek létehozása
fokompok = prcomp(df[,c("VEDONO", "HO_FORG_RB", "HO_FORG_OSSZ", "FGYHO_SZOLG_SZAM", "HO_SZOLG_SZAM", "HO_APOLO_SZAM", "HGYO_SZAM")], center = TRUE, scale=TRUE)
summary(fokompok)
## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6 PC7
## Standard deviation 2.0970 1.0020 0.83167 0.6787 0.50551 0.38691 0.20237
## Proportion of Variance 0.6282 0.1434 0.09881 0.0658 0.03651 0.02139 0.00585
## Cumulative Proportion 0.6282 0.7716 0.87045 0.9363 0.97276 0.99415 1.00000
df = cbind(df, fokompok$x[,1:3])
corrplot(cor(df[,c("VEDONO", "HO_FORG_RB", "HO_FORG_OSSZ", "FGYHO_SZOLG_SZAM", "HO_SZOLG_SZAM", "HO_APOLO_SZAM", "HGYO_SZAM", "PC1", "PC2", "PC3")]), method = "color", addCoef.col = "black")
Ez így nem jó, nem lehet meghatározni a PC2 és PC3-at, mert mind a VEDONO, mind a HGYO_SZAM korrelál vele.
fokompok = prcomp(df[,c("HO_FORG_RB", "HO_FORG_OSSZ", "FGYHO_SZOLG_SZAM", "HO_SZOLG_SZAM", "HO_APOLO_SZAM", "HGYO_SZAM")], center = TRUE, scale=TRUE)
summary(fokompok)
## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6
## Standard deviation 2.0516 0.9336 0.68701 0.50628 0.3873 0.20237
## Proportion of Variance 0.7015 0.1453 0.07866 0.04272 0.0250 0.00683
## Cumulative Proportion 0.7015 0.8468 0.92545 0.96817 0.9932 1.00000
df[,c("PC1", "PC2")] = fokompok$x[,1:2]
Akkor a VEDONO és elhagyásával járok jobban(source: próbálgatás).
corrplot(cor(df[,c("VEDONO", "HO_FORG_RB", "HO_FORG_OSSZ", "FGYHO_SZOLG_SZAM", "HO_SZOLG_SZAM", "HO_APOLO_SZAM", "HGYO_SZAM", "PC1", "PC2")]), method = "color", addCoef.col = "black")
Mik lehetnek az egyes főkomponensek?
corrplot(cor(df[,c(4:8,14:16,19,38:40)], use = "complete.obs"), method = "color", addCoef.col = "darkolivegreen")
phtest(LAKAS_PRED ~ SZJA + MUNKA + BERUHAZAS + CSOKTREND + BUNOZES + SERTETT + PEDAGOGUS + ATLAGAR + VEDONO + PC1 + PC2, df, index = c("JARAS_NEV", "EV"))
##
## Hausman Test
##
## data: LAKAS_PRED ~ SZJA + MUNKA + BERUHAZAS + CSOKTREND + BUNOZES + ...
## chisq = 102.16, df = 12, p-value < 2.2e-16
## alternative hypothesis: one model is inconsistent
pFtest(LAKAS_PRED ~ SZJA + MUNKA + BERUHAZAS + CSOKTREND + BUNOZES + SERTETT + PEDAGOGUS + ATLAGAR + VEDONO + PC1 + PC2, df, index = c("JARAS_NEV", "EV"))
##
## F test for individual effects
##
## data: LAKAS_PRED ~ SZJA + MUNKA + BERUHAZAS + CSOKTREND + BUNOZES + ...
## F = 6.0579, df1 = 174, df2 = 1563, p-value < 2.2e-16
## alternative hypothesis: significant effects
Mindkét teszt p-értéke 2.2 \(\times\) 10-16, vagyis szinte 0. Ezt azt jelenti, hogy mindkét esetben el lehet vetni a nullhipotézist.
A Hausmann teszt nullhipotzise az, hogy a random modell a megfelelő a fix hatásúval szemben. Ezt elvethetjük.
A globális F próba nullhipotézise szerint a Pooled OLS modell a megfelelő, csakugyan a fix hatásúval szemben. Ezt is elvethetjük.
Tehát a panelmodell fix hatású lesz.
df2 = df %>% arrange(EV)
listw36 = nb2listw(dnearneigh(df2[df2$EV == "2016",c(9,10)], 0, 36, longlat = T), style = "W")
paic = function(model){
return(2 * (length(model$coefficients) + 1) - 2 * model$logLik)
}
pbic = function(model){
return(-2 * model$logLik + (length(model$coefficients)+1)*log(nrow(model$model)))
}
CSOK dummy: “nincs”, “alacsony”,“közepes”, “magas”
cuts = c(-Inf, 1, 70, 86, Inf)
labs = c("nincs", "alacsony","közepes", "magas")
df2$CSOKTREND = cut(df2$CSOK, breaks = cuts, labels = labs, include.lowest = T)
model1_0 = spml(LAKAS_PRED ~ SZJA + MUNKA + BERUHAZAS + CSOKTREND + BUNOZES + SERTETT + PEDAGOGUS + ATLAGAR + VEDONO + PC1 + PC2, df2,
listw = listw36, model = "within", index = c("JARAS_NEV", "EV"), lag = T,
effect = "individual", spatial.error = "none")
summary(model1_0)
## Spatial panel fixed effects lag model
##
##
## Call:
## spml(formula = LAKAS_PRED ~ SZJA + MUNKA + BERUHAZAS + CSOKTREND +
## BUNOZES + SERTETT + PEDAGOGUS + ATLAGAR + VEDONO + PC1 +
## PC2, data = df2, index = c("JARAS_NEV", "EV"), listw = listw36,
## model = "within", effect = "individual", lag = T, spatial.error = "none")
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -41.16936 -2.35093 -0.26746 1.85967 115.02179
##
## Spatial autoregressive coefficient:
## Estimate Std. Error t-value Pr(>|t|)
## lambda 0.334465 0.031949 10.469 < 2.2e-16 ***
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## SZJA -4.1749e-03 2.3311e-03 -1.7910 0.0732978 .
## MUNKA 1.8481e-02 1.0173e-01 0.1817 0.8558504
## BERUHAZAS -4.3045e-03 1.3503e-03 -3.1877 0.0014339 **
## CSOKTRENDalacsony 2.4397e+00 9.6247e-01 2.5348 0.0112502 *
## CSOKTRENDközepes 3.0051e+00 7.8888e-01 3.8093 0.0001394 ***
## CSOKTRENDmagas 7.3648e-01 7.4179e-01 0.9928 0.3207843
## BUNOZES -8.1145e-05 1.7088e-04 -0.4749 0.6348862
## SERTETT -6.0537e-04 3.9226e-04 -1.5433 0.1227604
## PEDAGOGUS 4.3912e-01 4.6188e-01 0.9507 0.3417377
## ATLAGAR 1.0233e+00 8.7992e-02 11.6290 < 2.2e-16 ***
## VEDONO -3.7488e-01 5.3680e-01 -0.6984 0.4849452
## PC1 -1.5222e+00 5.9662e-01 -2.5514 0.0107295 *
## PC2 -9.2588e-01 5.5001e-01 -1.6834 0.0923023 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
model1_1 = spml(LAKAS_PRED ~ SZJA + BERUHAZAS + CSOKTREND + SERTETT + ATLAGAR + PC1 + PC2, df2,
listw = listw36, model = "within", index = c("JARAS_NEV", "EV"), lag = T,
effect = "individual", spatial.error = "none")
summary(model1_1)
## Spatial panel fixed effects lag model
##
##
## Call:
## spml(formula = LAKAS_PRED ~ SZJA + BERUHAZAS + CSOKTREND + SERTETT +
## ATLAGAR + PC1 + PC2, data = df2, index = c("JARAS_NEV", "EV"),
## listw = listw36, model = "within", effect = "individual",
## lag = T, spatial.error = "none")
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -41.4239 -2.2894 -0.2844 1.8586 115.0847
##
## Spatial autoregressive coefficient:
## Estimate Std. Error t-value Pr(>|t|)
## lambda 0.336772 0.031794 10.592 < 2.2e-16 ***
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## SZJA -0.00425377 0.00179515 -2.3696 0.0178079 *
## BERUHAZAS -0.00426167 0.00134446 -3.1698 0.0015254 **
## CSOKTRENDalacsony 2.31263803 0.93010636 2.4864 0.0129034 *
## CSOKTRENDközepes 2.84225552 0.74755859 3.8021 0.0001435 ***
## CSOKTRENDmagas 0.59852118 0.65895894 0.9083 0.3637287
## SERTETT -0.00069041 0.00033112 -2.0851 0.0370634 *
## ATLAGAR 1.03972200 0.08411001 12.3615 < 2.2e-16 ***
## PC1 -1.55503557 0.58933697 -2.6386 0.0083245 **
## PC2 -0.95478725 0.54560891 -1.7499 0.0801273 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
paic(model1_0)
## [1] 11934.01
paic(model1_1)
## [1] 11927.62
pbic(model1_0)
## [1] 12016.02
pbic(model1_1)
## [1] 11987.76
AIC, BIC preferálja a szűkített modellt.
model1 = model1_1
CSOK dummy: “nincs”, “alacsony”, “magas”
cuts = c(-Inf, 1, 75, Inf)
labs = c("nincs", "alacsony", "magas")
df2$CSOKTREND = cut(df2$CSOK, breaks = cuts, labels = labs, include.lowest = T)
rm(cuts, labs)
model2_0 = spml(LAKAS_PRED ~ SZJA + MUNKA + BERUHAZAS + CSOKTREND + BUNOZES + SERTETT + PEDAGOGUS + ATLAGAR+ VEDONO + PC1 + PC2, df2,
listw = listw36, model = "within", index = c("JARAS_NEV", "EV"), lag = T,
effect = "individual", spatial.error = "none")
summary(model2_0)
## Spatial panel fixed effects lag model
##
##
## Call:
## spml(formula = LAKAS_PRED ~ SZJA + MUNKA + BERUHAZAS + CSOKTREND +
## BUNOZES + SERTETT + PEDAGOGUS + ATLAGAR + VEDONO + PC1 +
## PC2, data = df2, index = c("JARAS_NEV", "EV"), listw = listw36,
## model = "within", effect = "individual", lag = T, spatial.error = "none")
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -40.93310 -2.31098 -0.29695 1.82221 114.95407
##
## Spatial autoregressive coefficient:
## Estimate Std. Error t-value Pr(>|t|)
## lambda 0.339461 0.031887 10.646 < 2.2e-16 ***
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## SZJA -3.2944e-03 2.3058e-03 -1.4287 0.153078
## MUNKA -1.9731e-02 1.0168e-01 -0.1940 0.846141
## BERUHAZAS -4.3958e-03 1.3543e-03 -3.2457 0.001172 **
## CSOKTRENDalacsony 2.6056e+00 8.6586e-01 3.0093 0.002619 **
## CSOKTRENDmagas 1.4993e+00 6.9522e-01 2.1566 0.031034 *
## BUNOZES -7.6554e-05 1.7124e-04 -0.4471 0.654835
## SERTETT -6.6491e-04 3.9215e-04 -1.6956 0.089970 .
## PEDAGOGUS 3.4458e-01 4.6233e-01 0.7453 0.456091
## ATLAGAR 9.9054e-01 8.7231e-02 11.3554 < 2.2e-16 ***
## VEDONO -4.4166e-01 5.3613e-01 -0.8238 0.410054
## PC1 -1.5278e+00 5.9779e-01 -2.5558 0.010595 *
## PC2 -9.2257e-01 5.5183e-01 -1.6718 0.094556 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
model2_1 = spml(LAKAS_PRED ~ SZJA + BERUHAZAS + CSOKTREND + SERTETT + ATLAGAR + PC1 + PC2, df2,
listw = listw36, model = "within", index = c("JARAS_NEV", "EV"), lag = T,
effect = "individual", spatial.error = "none")
summary(model2_1)
## Spatial panel fixed effects lag model
##
##
## Call:
## spml(formula = LAKAS_PRED ~ SZJA + BERUHAZAS + CSOKTREND + SERTETT +
## ATLAGAR + PC1 + PC2, data = df2, index = c("JARAS_NEV", "EV"),
## listw = listw36, model = "within", effect = "individual",
## lag = T, spatial.error = "none")
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -41.18165 -2.26755 -0.32048 1.80800 114.93781
##
## Spatial autoregressive coefficient:
## Estimate Std. Error t-value Pr(>|t|)
## lambda 0.342387 0.031719 10.794 < 2.2e-16 ***
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## SZJA -0.00391371 0.00177554 -2.2042 0.027507 *
## BERUHAZAS -0.00438735 0.00134755 -3.2558 0.001131 **
## CSOKTRENDalacsony 2.42113995 0.83234196 2.9088 0.003628 **
## CSOKTRENDmagas 1.27833311 0.61956739 2.0633 0.039087 *
## SERTETT -0.00072349 0.00033115 -2.1848 0.028906 *
## ATLAGAR 1.01539173 0.08365404 12.1380 < 2.2e-16 ***
## PC1 -1.58840188 0.59045580 -2.6901 0.007142 **
## PC2 -0.97213636 0.54700891 -1.7772 0.075538 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
paic(model2_0)
## [1] 11940.1
paic(model2_1)
## [1] 11933.74
pbic(model2_0)
## [1] 12016.64
pbic(model2_1)
## [1] 11988.41
AIC, BIC preferálja a szűkített modellt.
model2 = model2_1
CSOK számként
model3_0 = spml(LAKAS_PRED ~ SZJA + MUNKA + BERUHAZAS + CSOK + BUNOZES + SERTETT + PEDAGOGUS + ATLAGAR + VEDONO + PC1 + PC2, df2,
listw = listw36, model = "within", index = c("JARAS_NEV", "EV"), lag = T,
effect = "individual", spatial.error = "none")
summary(model3_0)
## Spatial panel fixed effects lag model
##
##
## Call:
## spml(formula = LAKAS_PRED ~ SZJA + MUNKA + BERUHAZAS + CSOK +
## BUNOZES + SERTETT + PEDAGOGUS + ATLAGAR + VEDONO + PC1 +
## PC2, data = df2, index = c("JARAS_NEV", "EV"), listw = listw36,
## model = "within", effect = "individual", lag = T, spatial.error = "none")
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -41.68965 -2.28237 -0.29391 1.79334 115.08427
##
## Spatial autoregressive coefficient:
## Estimate Std. Error t-value Pr(>|t|)
## lambda 0.352225 0.031469 11.193 < 2.2e-16 ***
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## SZJA -1.5797e-03 2.1730e-03 -0.7270 0.467246
## MUNKA -5.0121e-02 1.0080e-01 -0.4972 0.619038
## BERUHAZAS -4.4124e-03 1.3557e-03 -3.2546 0.001135 **
## CSOK 1.4477e-02 7.6444e-03 1.8938 0.058245 .
## BUNOZES -7.6084e-05 1.7134e-04 -0.4440 0.657009
## SERTETT -6.9099e-04 3.9224e-04 -1.7617 0.078127 .
## PEDAGOGUS 1.8803e-01 4.5788e-01 0.4106 0.681330
## ATLAGAR 1.0011e+00 8.7568e-02 11.4318 < 2.2e-16 ***
## VEDONO -5.0964e-01 5.3570e-01 -0.9514 0.341427
## PC1 -1.5722e+00 5.9727e-01 -2.6323 0.008480 **
## PC2 -8.5015e-01 5.5112e-01 -1.5426 0.122933
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
model3_1 = spml(LAKAS_PRED ~ SZJA + BERUHAZAS + CSOK + SERTETT + ATLAGAR + PC1 + PC2, df2,
listw = listw36, model = "within", index = c("JARAS_NEV", "EV"), lag = T,
effect = "individual", spatial.error = "none")
summary(model3_1)
## Spatial panel fixed effects lag model
##
##
## Call:
## spml(formula = LAKAS_PRED ~ SZJA + BERUHAZAS + CSOK + SERTETT +
## ATLAGAR + PC1 + PC2, data = df2, index = c("JARAS_NEV", "EV"),
## listw = listw36, model = "within", effect = "individual",
## lag = T, spatial.error = "none")
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -41.8629 -2.2233 -0.3199 1.7481 114.9608
##
## Spatial autoregressive coefficient:
## Estimate Std. Error t-value Pr(>|t|)
## lambda 0.355354 0.031325 11.344 < 2.2e-16 ***
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## SZJA -0.00257700 0.00167474 -1.5387 0.1238661
## BERUHAZAS -0.00445650 0.00134833 -3.3052 0.0009491 ***
## CSOK 0.01171637 0.00678153 1.7277 0.0840440 .
## SERTETT -0.00074205 0.00033131 -2.2397 0.0251086 *
## ATLAGAR 1.02811862 0.08416086 12.2161 < 2.2e-16 ***
## PC1 -1.64544396 0.58951316 -2.7912 0.0052514 **
## PC2 -0.90554422 0.54682958 -1.6560 0.0977239 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
model3_2 = spml(LAKAS_PRED ~ BERUHAZAS + CSOK + SERTETT + ATLAGAR + PC1 + PC2, df2,
listw = listw36, model = "within", index = c("JARAS_NEV", "EV"), lag = T,
effect = "individual", spatial.error = "none")
summary(model3_2)
## Spatial panel fixed effects lag model
##
##
## Call:
## spml(formula = LAKAS_PRED ~ BERUHAZAS + CSOK + SERTETT + ATLAGAR +
## PC1 + PC2, data = df2, index = c("JARAS_NEV", "EV"), listw = listw36,
## model = "within", effect = "individual", lag = T, spatial.error = "none")
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -40.79970 -2.28129 -0.25005 1.77701 115.29025
##
## Spatial autoregressive coefficient:
## Estimate Std. Error t-value Pr(>|t|)
## lambda 0.347068 0.031386 11.058 < 2.2e-16 ***
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## BERUHAZAS -0.00566403 0.00108974 -5.1976 2.019e-07 ***
## CSOK 0.00623925 0.00573560 1.0878 0.27668
## SERTETT -0.00062998 0.00032326 -1.9489 0.05131 .
## ATLAGAR 0.94973092 0.06603832 14.3815 < 2.2e-16 ***
## PC1 -1.83090936 0.58016963 -3.1558 0.00160 **
## PC2 -0.91763770 0.54747765 -1.6761 0.09371 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
paic(model3_0)
## [1] 11943.54
paic(model3_1)
## [1] 11937.2
paic(model3_2)
## [1] 11937.51
pbic(model3_0)
## [1] 12014.62
pbic(model3_1)
## [1] 11986.4
pbic(model3_2)
## [1] 11981.24
AIC modell3_1-et preferálja, BIC modell3_2-t. BIC mellett döntök.
model3 = model3_2
CSOK nélkül
model4_0 = spml(LAKAS_PRED ~ SZJA + MUNKA + BERUHAZAS + BUNOZES + SERTETT + PEDAGOGUS + ATLAGAR + VEDONO + PC1 + PC2, df2,
listw = listw36, model = "within", index = c("JARAS_NEV", "EV"), lag = T,
effect = "individual", spatial.error = "none")
summary(model4_0)
## Spatial panel fixed effects lag model
##
##
## Call:
## spml(formula = LAKAS_PRED ~ SZJA + MUNKA + BERUHAZAS + BUNOZES +
## SERTETT + PEDAGOGUS + ATLAGAR + VEDONO + PC1 + PC2, data = df2,
## index = c("JARAS_NEV", "EV"), listw = listw36, model = "within",
## effect = "individual", lag = T, spatial.error = "none")
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -41.54154 -2.24867 -0.27788 1.74321 115.04944
##
## Spatial autoregressive coefficient:
## Estimate Std. Error t-value Pr(>|t|)
## lambda 0.354034 0.031468 11.251 < 2.2e-16 ***
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## SZJA -1.1122e-03 2.1606e-03 -0.5147 0.6067309
## MUNKA 1.8616e-02 9.4067e-02 0.1979 0.8431241
## BERUHAZAS -4.9742e-03 1.3242e-03 -3.7563 0.0001724 ***
## BUNOZES -5.9768e-05 1.7128e-04 -0.3490 0.7271183
## SERTETT -7.4981e-04 3.9133e-04 -1.9160 0.0553604 .
## PEDAGOGUS 8.9962e-02 4.5531e-01 0.1976 0.8433712
## ATLAGAR 9.8124e-01 8.6948e-02 11.2854 < 2.2e-16 ***
## VEDONO -4.9154e-01 5.3609e-01 -0.9169 0.3592027
## PC1 -1.6061e+00 5.9745e-01 -2.6882 0.0071836 **
## PC2 -8.5975e-01 5.5158e-01 -1.5587 0.1190691
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
model4_1 = spml(LAKAS_PRED ~ SZJA + BERUHAZAS + SERTETT + ATLAGAR + PC1 + PC2, df2,
listw = listw36, model = "within", index = c("JARAS_NEV", "EV"), lag = T,
effect = "individual", spatial.error = "none")
summary(model4_1)
## Spatial panel fixed effects lag model
##
##
## Call:
## spml(formula = LAKAS_PRED ~ SZJA + BERUHAZAS + SERTETT + ATLAGAR +
## PC1 + PC2, data = df2, index = c("JARAS_NEV", "EV"), listw = listw36,
## model = "within", effect = "individual", lag = T, spatial.error = "none")
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -41.56431 -2.19050 -0.29791 1.74012 114.99265
##
## Spatial autoregressive coefficient:
## Estimate Std. Error t-value Pr(>|t|)
## lambda 0.355043 0.031365 11.32 < 2.2e-16 ***
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## SZJA -0.00101883 0.00141572 -0.7197 0.4717404
## BERUHAZAS -0.00504408 0.00130583 -3.8627 0.0001121 ***
## SERTETT -0.00084647 0.00032605 -2.5961 0.0094280 **
## ATLAGAR 0.98741472 0.08078999 12.2220 < 2.2e-16 ***
## PC1 -1.60051201 0.58947899 -2.7151 0.0066250 **
## PC2 -0.86230741 0.54672195 -1.5772 0.1147421
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
model4_2 = spml(LAKAS_PRED ~ BERUHAZAS + SERTETT + ATLAGAR + PC1 + PC2, df2,
listw = listw36, model = "within", index = c("JARAS_NEV", "EV"), lag = T,
effect = "individual", spatial.error = "none")
summary(model4_2)
## Spatial panel fixed effects lag model
##
##
## Call:
## spml(formula = LAKAS_PRED ~ BERUHAZAS + SERTETT + ATLAGAR + PC1 +
## PC2, data = df2, index = c("JARAS_NEV", "EV"), listw = listw36,
## model = "within", effect = "individual", lag = T, spatial.error = "none")
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -41.05520 -2.22611 -0.27883 1.74199 115.16601
##
## Spatial autoregressive coefficient:
## Estimate Std. Error t-value Pr(>|t|)
## lambda 0.350561 0.031256 11.216 < 2.2e-16 ***
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## BERUHAZAS -0.00555821 0.00108471 -5.1241 2.989e-07 ***
## SERTETT -0.00075780 0.00030051 -2.5218 0.011677 *
## ATLAGAR 0.95469555 0.06592222 14.4822 < 2.2e-16 ***
## PC1 -1.71429181 0.57149834 -2.9996 0.002703 **
## PC2 -0.88010852 0.54652397 -1.6104 0.107316
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
model4_3 = spml(LAKAS_PRED ~ BERUHAZAS + SERTETT + ATLAGAR + PC1, df2,
listw = listw36, model = "within", index = c("JARAS_NEV", "EV"), lag = T,
effect = "individual", spatial.error = "none")
summary(model4_3)
## Spatial panel fixed effects lag model
##
##
## Call:
## spml(formula = LAKAS_PRED ~ BERUHAZAS + SERTETT + ATLAGAR + PC1,
## data = df2, index = c("JARAS_NEV", "EV"), listw = listw36,
## model = "within", effect = "individual", lag = T, spatial.error = "none")
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -41.43348 -2.17797 -0.28075 1.79870 115.13108
##
## Spatial autoregressive coefficient:
## Estimate Std. Error t-value Pr(>|t|)
## lambda 0.352327 0.031262 11.27 < 2.2e-16 ***
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## BERUHAZAS -0.00566695 0.00108341 -5.2307 1.689e-07 ***
## SERTETT -0.00077183 0.00030058 -2.5678 0.010234 *
## ATLAGAR 0.96881351 0.06533304 14.8288 < 2.2e-16 ***
## PC1 -1.78677147 0.56980191 -3.1358 0.001714 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
paic(model4_0)
## [1] 11945.14
paic(model4_1)
## [1] 11938.18
paic(model4_2)
## [1] 11936.68
paic(model4_3)
## [1] 11937.27
pbic(model4_0)
## [1] 12010.75
pbic(model4_1)
## [1] 11981.92
pbic(model4_2)
## [1] 11974.96
pbic(model4_3)
## [1] 11970.08
AIC modell4_2-t preferalja, BIC modell4_3/t, BIC szerint dontok.
model4 = model4_3
#valamiert nem mukodik a lapply/sapply... -val :/
data.frame(AIC = c(paic(model1), paic(model2), paic(model3), paic(model4)) ,
BIC = c(pbic(model1), pbic(model2), pbic(model3), pbic(model4)))
## AIC BIC
## 1 11927.62 11987.76
## 2 11933.74 11988.41
## 3 11937.51 11981.24
## 4 11937.27 11970.08
sem = "
# measurement model
EU =~ VEDONO + HO_FORG_RB + HO_FORG_OSSZ + FGYHO_SZOLG_SZAM + HO_SZOLG_SZAM + HO_APOLO_SZAM + HGYO_SZAM
# regressions
LAKAS_PRED ~ SZJA + MUNKA + ATLAGAR + EU
ATLAGAR ~ SZJA + EU
SZJA ~ MUNKA + BERUHAZAS + EU
# residual correlations
VEDONO ~~ HO_APOLO_SZAM + HO_SZAM + HO_FORG_OSSZ + HO_FORG_RB
HO_FORG_RB ~~ HGYO_SZAM + HO_APOLO_SZAM + HO_SZOLG_SZAM + FGYHO_SZOLG_SZAM + HO_FORG_OSSZ
HO_FORG_OSSZ ~~ HGYO_SZAM + HO_APOLO_SZAM + HO_SZOLG_SZAM + FGYHO_SZOLG_SZAM
FGYHO_SZOLG_SZAM ~~ HGYO_SZAM + HO_APOLO_SZAM + HO_SZOLG_SZAM
HO_SZOLG_SZAM ~~ HO_APOLO_SZAM + HGYO_SZAM
HO_APOLO_SZAM ~~ HGYO_SZAM
"
fit = sem(sem, data = df2012)
## Warning in lav_data_full(data = data, group = group, cluster = cluster, :
## lavaan WARNING: some observed variances are (at least) a factor 1000 times
## larger than others; use varTable(fit) to investigate
## Warning in lav_data_full(data = data, group = group, cluster = cluster, : lavaan WARNING: some observed variances are larger than 1000000
## lavaan NOTE: use varTable(fit) to investigate
## Warning in lav_model_vcov(lavmodel = lavmodel, lavsamplestats = lavsamplestats, : lavaan WARNING:
## Could not compute standard errors! The information matrix could
## not be inverted. This may be a symptom that the model is not
## identified.
## Warning in lav_object_post_check(object): lavaan WARNING: the covariance matrix of the residuals of the observed
## variables (theta) is not positive definite;
## use lavInspect(fit, "theta") to investigate.
summary(fit, standardized = T)
## lavaan 0.6.16 ended normally after 435 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 46
##
## Number of observations 175
##
## Model Test User Model:
##
## Test statistic 403.731
## Degrees of freedom 42
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## EU =~
## VEDONO 1.000 0.145 0.197
## HO_FORG_RB 83746.078 NA 12171.611 0.997
## HO_FORG_OSSZ 84683.239 NA 12307.818 0.999
## FGYHO_SZOLG_SZ 7.291 NA 1.060 0.706
## HO_SZOLG_SZAM 4.510 NA 0.656 0.768
## HO_APOLO_SZAM 5.238 NA 0.761 0.667
## HGYO_SZAM -1.474 NA -0.214 -0.537
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## LAKAS_PRED ~
## SZJA -0.010 NA -0.010 -0.157
## MUNKA 0.086 NA 0.086 0.041
## ATLAGAR 2.001 NA 2.001 0.786
## EU -4.699 NA -0.683 -0.074
## ATLAGAR ~
## SZJA 0.012 NA 0.012 0.496
## EU -10.516 NA -1.528 -0.424
## SZJA ~
## MUNKA 12.136 NA 12.136 0.357
## BERUHAZAS 0.072 NA 0.072 0.077
## EU -557.628 NA -81.045 -0.540
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .VEDONO ~~
## .HO_APOLO_SZAM 0.049 NA 0.049 0.079
## HO_SZAM 0.160 NA 0.160 0.293
## .HO_FORG_OSSZ 145.980 NA 145.980 0.424
## .HO_FORG_RB 273.911 NA 273.911 0.410
## .HO_FORG_RB ~~
## .HGYO_SZAM -51.964 NA -51.964 -0.167
## .HO_APOLO_SZAM 256.676 NA 256.676 0.327
## .HO_SZOLG_SZAM -461.975 NA -461.975 -0.915
## .FGYHO_SZOLG_SZ 208.868 NA 208.868 0.213
## .HO_FORG_OSSZ -0.010 NA -0.010 -0.000
## .HO_FORG_OSSZ ~~
## .HGYO_SZAM -48.705 NA -48.705 -0.305
## .HO_APOLO_SZAM 230.148 NA 230.148 0.570
## .HO_SZOLG_SZAM -531.838 NA -531.838 -2.046
## .FGYHO_SZOLG_SZ 214.652 NA 214.652 0.426
## .FGYHO_SZOLG_SZAM ~~
## .HGYO_SZAM -0.182 NA -0.182 -0.510
## .HO_APOLO_SZAM 0.333 NA 0.333 0.368
## .HO_SZOLG_SZAM 0.218 NA 0.218 0.376
## .HO_SZOLG_SZAM ~~
## .HO_APOLO_SZAM 0.296 NA 0.296 0.637
## .HGYO_SZAM 0.014 NA 0.014 0.076
## .HO_APOLO_SZAM ~~
## .HGYO_SZAM -0.029 NA -0.029 -0.100
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .VEDONO 0.525 NA 0.525 0.961
## .HO_FORG_RB 851301.145 NA 851301.145 0.006
## .HO_FORG_OSSZ 225579.062 NA 225579.062 0.001
## .FGYHO_SZOLG_SZ 1.127 NA 1.127 0.501
## .HO_SZOLG_SZAM 0.300 NA 0.300 0.411
## .HO_APOLO_SZAM 0.724 NA 0.724 0.555
## .HGYO_SZAM 0.113 NA 0.113 0.712
## .LAKAS_PRED 38.044 NA 38.044 0.452
## .ATLAGAR 4.506 NA 4.506 0.347
## .SZJA 12142.463 NA 12142.463 0.540
## HO_SZAM 0.567 NA 0.567 1.000
## EU 0.021 NA 1.000 1.000
vartable(fit)
## name idx nobs type exo user mean var nlev lnam
## 1 VEDONO 37 175 numeric 0 0 5.141 5.820000e-01 0
## 2 HO_FORG_RB 35 175 numeric 0 0 63569.589 1.498855e+08 0
## 3 HO_FORG_OSSZ 31 175 numeric 0 0 66120.469 1.525984e+08 0
## 4 FGYHO_SZOLG_SZAM 26 175 numeric 0 0 2.585 2.264000e+00 0
## 5 HO_SZOLG_SZAM 23 175 numeric 0 0 5.354 7.320000e-01 0
## 6 HO_APOLO_SZAM 22 175 numeric 0 0 5.996 1.312000e+00 0
## 7 HGYO_SZAM 21 175 numeric 0 0 1.310 1.600000e-01 0
## 8 LAKAS_PRED 38 175 numeric 0 0 8.176 8.976800e+01 0
## 9 ATLAGAR 19 175 numeric 0 0 6.446 1.461600e+01 0
## 10 SZJA 5 175 numeric 0 0 692.955 2.686650e+04 0
## 11 MUNKA 6 175 numeric 1 0 54.795 1.960900e+01 0
## 12 BERUHAZAS 7 175 numeric 1 0 339.526 2.575309e+04 0
## 13 HO_SZAM 20 175 numeric 0 0 5.012 5.700000e-01 0
# df2012$LAKAS_PRED = scale(df2012$LAKAS_PRED)
# df2012$n_HO_FORG_RB = scale(df2012$HO_FORG_RB)
# df2012$n_HO_FORG_OSSZ = scale(df2012$HO_FORG_OSSZ)
# df2012$n_SZJA = scale(df2012$SZJA)
# df2012$n_BERUHAZAS = scale(df2012$BERUHAZAS)
n_df2012 = scale((select_if(df2012[,-81], is.numeric)))
sem = "
# measurement model
EU =~ VEDONO + HO_FORG_RB + HO_FORG_OSSZ + FGYHO_SZOLG_SZAM + HO_SZOLG_SZAM + HO_APOLO_SZAM + HGYO_SZAM
# regressions
LAKAS_PRED ~ SZJA + BERUHAZAS + MUNKA + ATLAGAR + EU
ATLAGAR ~ SZJA + EU
SZJA ~ MUNKA + BERUHAZAS + EU
# residual correlations
VEDONO ~~ HO_APOLO_SZAM + HO_SZAM + HO_FORG_OSSZ + HO_FORG_RB
HO_FORG_RB ~~ HGYO_SZAM + HO_APOLO_SZAM + HO_SZOLG_SZAM + FGYHO_SZOLG_SZAM + HO_FORG_OSSZ
HO_FORG_OSSZ ~~ HGYO_SZAM + HO_APOLO_SZAM + HO_SZOLG_SZAM + FGYHO_SZOLG_SZAM
FGYHO_SZOLG_SZAM ~~ HGYO_SZAM + HO_APOLO_SZAM + HO_SZOLG_SZAM
HO_SZOLG_SZAM ~~ HO_APOLO_SZAM + HGYO_SZAM
HO_APOLO_SZAM ~~ HGYO_SZAM
"
fit = sem(sem, data = n_df2012)
summary(fit, standardized = T)
## lavaan 0.6.16 ended normally after 177 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 47
##
## Number of observations 175
##
## Model Test User Model:
##
## Test statistic 396.082
## Degrees of freedom 41
## P-value (Chi-square) 0.000
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## EU =~
## VEDONO 1.000 0.159 0.163
## HO_FORG_RB 5.255 2.531 2.076 0.038 0.838 0.841
## HO_FORG_OSSZ 5.255 2.539 2.070 0.038 0.838 0.841
## FGYHO_SZOLG_SZ 3.524 1.767 1.995 0.046 0.562 0.563
## HO_SZOLG_SZAM 3.938 1.955 2.014 0.044 0.628 0.630
## HO_APOLO_SZAM 3.497 1.721 2.033 0.042 0.558 0.560
## HGYO_SZAM -2.750 1.420 -1.937 0.053 -0.438 -0.440
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## LAKAS_PRED ~
## SZJA -0.226 0.095 -2.379 0.017 -0.226 -0.206
## BERUHAZAS 0.155 0.064 2.415 0.016 0.155 0.153
## MUNKA -0.048 0.069 -0.689 0.491 -0.048 -0.047
## ATLAGAR 0.730 0.164 4.440 0.000 0.730 0.700
## EU -1.440 1.364 -1.056 0.291 -0.230 -0.228
## ATLAGAR ~
## SZJA 0.305 0.117 2.597 0.009 0.305 0.290
## EU -4.007 2.110 -1.899 0.058 -0.639 -0.662
## SZJA ~
## MUNKA 0.281 0.064 4.383 0.000 0.281 0.305
## BERUHAZAS 0.110 0.063 1.740 0.082 0.110 0.120
## EU -3.798 1.893 -2.007 0.045 -0.606 -0.659
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .VEDONO ~~
## .HO_APOLO_SZAM 0.059 0.039 1.510 0.131 0.059 0.074
## HO_SZAM 0.319 0.075 4.258 0.000 0.319 0.332
## .HO_FORG_OSSZ 0.039 0.038 1.046 0.296 0.039 0.076
## .HO_FORG_RB 0.053 0.038 1.401 0.161 0.053 0.101
## .HO_FORG_RB ~~
## .HGYO_SZAM -0.175 0.068 -2.572 0.010 -0.175 -0.363
## .HO_APOLO_SZAM 0.211 0.075 2.812 0.005 0.211 0.473
## .HO_SZOLG_SZAM 0.191 0.079 2.416 0.016 0.191 0.456
## .FGYHO_SZOLG_SZ 0.239 0.076 3.125 0.002 0.239 0.537
## .HO_FORG_OSSZ 0.287 0.096 3.006 0.003 0.287 0.988
## .HO_FORG_OSSZ ~~
## .HGYO_SZAM -0.176 0.068 -2.575 0.010 -0.176 -0.364
## .HO_APOLO_SZAM 0.210 0.075 2.800 0.005 0.210 0.472
## .HO_SZOLG_SZAM 0.186 0.079 2.357 0.018 0.186 0.445
## .FGYHO_SZOLG_SZ 0.241 0.077 3.141 0.002 0.241 0.541
## .FGYHO_SZOLG_SZAM ~~
## .HGYO_SZAM -0.434 0.078 -5.558 0.000 -0.434 -0.588
## .HO_APOLO_SZAM 0.347 0.076 4.578 0.000 0.347 0.510
## .HO_SZOLG_SZAM 0.355 0.078 4.573 0.000 0.355 0.556
## .HO_SZOLG_SZAM ~~
## .HO_APOLO_SZAM 0.461 0.081 5.703 0.000 0.461 0.720
## .HGYO_SZAM -0.093 0.069 -1.361 0.173 -0.093 -0.135
## .HO_APOLO_SZAM ~~
## .HGYO_SZAM -0.172 0.070 -2.470 0.014 -0.172 -0.233
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .VEDONO 0.931 0.100 9.345 0.000 0.931 0.973
## .HO_FORG_RB 0.291 0.096 3.042 0.002 0.291 0.293
## .HO_FORG_OSSZ 0.291 0.096 3.039 0.002 0.291 0.293
## .FGYHO_SZOLG_SZ 0.679 0.090 7.506 0.000 0.679 0.683
## .HO_SZOLG_SZAM 0.600 0.089 6.732 0.000 0.600 0.604
## .HO_APOLO_SZAM 0.681 0.089 7.624 0.000 0.681 0.687
## .HGYO_SZAM 0.802 0.095 8.468 0.000 0.802 0.807
## .LAKAS_PRED 0.404 0.046 8.811 0.000 0.404 0.398
## .ATLAGAR 0.210 0.058 3.644 0.000 0.210 0.225
## .SZJA 0.348 0.061 5.738 0.000 0.348 0.412
## HO_SZAM 0.994 0.106 9.354 0.000 0.994 1.000
## EU 0.025 0.025 1.025 0.305 1.000 1.000
lavaanPlot(model = fit, node_options = list(shape = "box", fontname = "Helvetica"), edge_options = list(color = "grey"), coefs = T)
sem = "
# measurement model
EU =~ HO_FORG_RB + HO_FORG_OSSZ + FGYHO_SZOLG_SZAM + HO_SZOLG_SZAM + HO_APOLO_SZAM
# regressions
LAKAS_PRED ~ SZJA + BERUHAZAS + MUNKA + ATLAGAR + EU
ATLAGAR ~ SZJA + EU
SZJA ~ MUNKA + BERUHAZAS + EU
# residual correlations
HO_FORG_RB ~~ HGYO_SZAM + HO_APOLO_SZAM + HO_SZOLG_SZAM + FGYHO_SZOLG_SZAM + HO_FORG_OSSZ
HO_FORG_OSSZ ~~ HGYO_SZAM + HO_APOLO_SZAM + HO_SZOLG_SZAM + FGYHO_SZOLG_SZAM
FGYHO_SZOLG_SZAM ~~ HGYO_SZAM + HO_APOLO_SZAM + HO_SZOLG_SZAM
HO_SZOLG_SZAM ~~ HO_APOLO_SZAM + HGYO_SZAM
HO_APOLO_SZAM ~~ HGYO_SZAM
"
fit = sem(sem, data = n_df2012)
summary(fit, standardized = T, fit.measures = T)
## lavaan 0.6.16 ended normally after 104 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 39
##
## Number of observations 175
##
## Model Test User Model:
##
## Test statistic 129.925
## Degrees of freedom 24
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 2093.767
## Degrees of freedom 54
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.948
## Tucker-Lewis Index (TLI) 0.883
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -1248.394
## Loglikelihood unrestricted model (H1) -1183.432
##
## Akaike (AIC) 2574.788
## Bayesian (BIC) 2698.214
## Sample-size adjusted Bayesian (SABIC) 2574.713
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.159
## 90 Percent confidence interval - lower 0.133
## 90 Percent confidence interval - upper 0.186
## P-value H_0: RMSEA <= 0.050 0.000
## P-value H_0: RMSEA >= 0.080 1.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.191
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## EU =~
## HO_FORG_RB 1.000 0.699 0.757
## HO_FORG_OSSZ 1.001 0.011 93.278 0.000 0.699 0.758
## FGYHO_SZOLG_SZ 0.424 0.077 5.540 0.000 0.297 0.315
## HO_SZOLG_SZAM 0.790 0.088 9.015 0.000 0.552 0.574
## HO_APOLO_SZAM 0.640 0.092 6.946 0.000 0.447 0.467
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## LAKAS_PRED ~
## SZJA -0.198 0.088 -2.235 0.025 -0.198 -0.182
## BERUHAZAS 0.150 0.064 2.356 0.018 0.150 0.149
## MUNKA -0.045 0.071 -0.633 0.526 -0.045 -0.045
## ATLAGAR 0.715 0.180 3.966 0.000 0.715 0.684
## EU -0.319 0.276 -1.158 0.247 -0.223 -0.223
## ATLAGAR ~
## SZJA 0.365 0.107 3.417 0.001 0.365 0.351
## EU -0.863 0.221 -3.911 0.000 -0.603 -0.630
## SZJA ~
## MUNKA 0.341 0.067 5.056 0.000 0.341 0.369
## BERUHAZAS 0.087 0.066 1.315 0.189 0.087 0.094
## EU -0.773 0.142 -5.444 0.000 -0.540 -0.586
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .HO_FORG_RB ~~
## HGYO_SZAM -0.324 0.059 -5.531 0.000 -0.324 -0.538
## .HO_APOLO_SZAM 0.261 0.073 3.596 0.000 0.261 0.511
## .HO_SZOLG_SZAM 0.232 0.078 2.960 0.003 0.232 0.489
## .FGYHO_SZOLG_SZ 0.346 0.066 5.236 0.000 0.346 0.643
## .HO_FORG_OSSZ 0.360 0.091 3.957 0.000 0.360 0.990
## .HO_FORG_OSSZ ~~
## HGYO_SZAM -0.324 0.058 -5.546 0.000 -0.324 -0.540
## .HO_APOLO_SZAM 0.260 0.073 3.584 0.000 0.260 0.510
## .HO_SZOLG_SZAM 0.227 0.078 2.900 0.004 0.227 0.479
## .FGYHO_SZOLG_SZ 0.347 0.066 5.250 0.000 0.347 0.646
## .FGYHO_SZOLG_SZAM ~~
## HGYO_SZAM -0.588 0.081 -7.213 0.000 -0.588 -0.660
## .HO_APOLO_SZAM 0.423 0.073 5.790 0.000 0.423 0.559
## .HO_SZOLG_SZAM 0.424 0.073 5.816 0.000 0.424 0.602
## .HO_SZOLG_SZAM ~~
## .HO_APOLO_SZAM 0.489 0.081 6.067 0.000 0.489 0.733
## HGYO_SZAM -0.196 0.065 -3.021 0.003 -0.196 -0.250
## .HO_APOLO_SZAM ~~
## HGYO_SZAM -0.277 0.069 -3.984 0.000 -0.277 -0.328
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .HO_FORG_RB 0.364 0.091 3.996 0.000 0.364 0.427
## .HO_FORG_OSSZ 0.363 0.091 3.985 0.000 0.363 0.426
## .FGYHO_SZOLG_SZ 0.797 0.089 8.931 0.000 0.797 0.901
## .HO_SZOLG_SZAM 0.621 0.090 6.902 0.000 0.621 0.671
## .HO_APOLO_SZAM 0.717 0.089 8.032 0.000 0.717 0.782
## .LAKAS_PRED 0.403 0.046 8.702 0.000 0.403 0.402
## .ATLAGAR 0.202 0.063 3.195 0.001 0.202 0.220
## .SZJA 0.398 0.064 6.233 0.000 0.398 0.468
## HGYO_SZAM 0.994 0.106 9.354 0.000 0.994 1.000
## EU 0.489 0.110 4.450 0.000 1.000 1.000
lavaanPlot(model = fit, node_options = list(shape = "box", fontname = "Helvetica"), edge_options = list(color = "grey"), coefs = T)
# knitr::include_graphics("tdk_files/figure-gfm/semplot.png")
# df2016$n_LAKAS_PRED = scale(df2016$LAKAS_PRED)
# df2016$n_HO_FORG_RB = scale(df2016$HO_FORG_RB)
# df2016$n_HO_FORG_OSSZ = scale(df2016$HO_FORG_OSSZ)
# df2016$n_SZJA = scale(df2016$SZJA)
# df2016$n_BERUHAZAS = scale(df2016$BERUHAZAS)
n_df2016 = scale((select_if(df2016[,-81], is.numeric)))
sem = "
# measurement model
EU =~ HO_FORG_RB + HO_FORG_OSSZ + FGYHO_SZOLG_SZAM + HO_SZOLG_SZAM + HO_APOLO_SZAM
# regressions
LAKAS_PRED ~ SZJA + BERUHAZAS + MUNKA + ATLAGAR + EU
ATLAGAR ~ SZJA + EU
SZJA ~ MUNKA + BERUHAZAS + EU
# residual correlations
HO_FORG_RB ~~ HGYO_SZAM + HO_APOLO_SZAM + HO_SZOLG_SZAM + FGYHO_SZOLG_SZAM + HO_FORG_OSSZ
HO_FORG_OSSZ ~~ HGYO_SZAM + HO_APOLO_SZAM + HO_SZOLG_SZAM + FGYHO_SZOLG_SZAM
FGYHO_SZOLG_SZAM ~~ HGYO_SZAM + HO_APOLO_SZAM + HO_SZOLG_SZAM
HO_SZOLG_SZAM ~~ HO_APOLO_SZAM + HGYO_SZAM
HO_APOLO_SZAM ~~ HGYO_SZAM
"
fit = sem(sem, data = n_df2016)
summary(fit, standardized = T, fit.measures = T)
## lavaan 0.6.16 ended normally after 89 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 39
##
## Number of observations 175
##
## Model Test User Model:
##
## Test statistic 115.454
## Degrees of freedom 24
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 2196.184
## Degrees of freedom 54
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.957
## Tucker-Lewis Index (TLI) 0.904
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -1189.950
## Loglikelihood unrestricted model (H1) -1132.223
##
## Akaike (AIC) 2457.901
## Bayesian (BIC) 2581.327
## Sample-size adjusted Bayesian (SABIC) 2457.826
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.148
## 90 Percent confidence interval - lower 0.121
## 90 Percent confidence interval - upper 0.175
## P-value H_0: RMSEA <= 0.050 0.000
## P-value H_0: RMSEA >= 0.080 1.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.192
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## EU =~
## HO_FORG_RB 1.000 0.823 0.881
## HO_FORG_OSSZ 1.000 0.006 175.989 0.000 0.823 0.882
## FGYHO_SZOLG_SZ 0.479 0.069 6.953 0.000 0.394 0.425
## HO_SZOLG_SZAM 0.815 0.076 10.716 0.000 0.671 0.683
## HO_APOLO_SZAM 0.725 0.078 9.342 0.000 0.597 0.614
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## LAKAS_PRED ~
## SZJA -0.263 0.100 -2.622 0.009 -0.263 -0.250
## BERUHAZAS 0.179 0.074 2.420 0.016 0.179 0.185
## MUNKA 0.071 0.069 1.019 0.308 0.071 0.073
## ATLAGAR 0.451 0.176 2.558 0.011 0.451 0.447
## EU -0.470 0.254 -1.851 0.064 -0.387 -0.400
## ATLAGAR ~
## SZJA 0.331 0.095 3.485 0.000 0.331 0.317
## EU -0.735 0.165 -4.441 0.000 -0.605 -0.631
## SZJA ~
## MUNKA 0.196 0.059 3.312 0.001 0.196 0.213
## BERUHAZAS 0.306 0.059 5.157 0.000 0.306 0.332
## EU -0.628 0.107 -5.885 0.000 -0.517 -0.563
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .HO_FORG_RB ~~
## HGYO_SZAM -0.210 0.050 -4.217 0.000 -0.210 -0.476
## .HO_APOLO_SZAM 0.126 0.082 1.537 0.124 0.126 0.372
## .HO_SZOLG_SZAM 0.089 0.088 1.014 0.311 0.089 0.281
## .FGYHO_SZOLG_SZ 0.212 0.066 3.190 0.001 0.212 0.572
## .HO_FORG_OSSZ 0.193 0.102 1.892 0.059 0.193 0.993
## .HO_FORG_OSSZ ~~
## HGYO_SZAM -0.212 0.050 -4.264 0.000 -0.212 -0.483
## .HO_APOLO_SZAM 0.127 0.082 1.543 0.123 0.127 0.375
## .HO_SZOLG_SZAM 0.086 0.088 0.980 0.327 0.086 0.272
## .FGYHO_SZOLG_SZ 0.211 0.066 3.182 0.001 0.211 0.572
## .FGYHO_SZOLG_SZAM ~~
## HGYO_SZAM -0.482 0.075 -6.451 0.000 -0.482 -0.576
## .HO_APOLO_SZAM 0.281 0.070 4.011 0.000 0.281 0.437
## .HO_SZOLG_SZAM 0.321 0.073 4.429 0.000 0.321 0.532
## .HO_SZOLG_SZAM ~~
## .HO_APOLO_SZAM 0.359 0.085 4.217 0.000 0.359 0.652
## HGYO_SZAM -0.058 0.061 -0.962 0.336 -0.058 -0.082
## .HO_APOLO_SZAM ~~
## HGYO_SZAM -0.120 0.063 -1.888 0.059 -0.120 -0.156
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .HO_FORG_RB 0.195 0.102 1.910 0.056 0.195 0.223
## .HO_FORG_OSSZ 0.194 0.102 1.899 0.058 0.194 0.222
## .FGYHO_SZOLG_SZ 0.705 0.084 8.385 0.000 0.705 0.819
## .HO_SZOLG_SZAM 0.516 0.095 5.408 0.000 0.516 0.534
## .HO_APOLO_SZAM 0.588 0.091 6.447 0.000 0.588 0.623
## .LAKAS_PRED 0.488 0.059 8.228 0.000 0.488 0.521
## .ATLAGAR 0.255 0.060 4.271 0.000 0.255 0.277
## .SZJA 0.377 0.056 6.744 0.000 0.377 0.447
## HGYO_SZAM 0.994 0.106 9.354 0.000 0.994 1.000
## EU 0.678 0.132 5.143 0.000 1.000 1.000
lavaanPlot(model = fit, node_options = list(shape = "box", fontname = "Helvetica"), edge_options = list(color = "grey"), coefs = T)
save_png(lavaanPlot(model = fit, node_options = list(shape = "box", fontname = "Helvetica"), edge_options = list(color = "grey"), coefs = T), "SEMplot.png")
# df2019$LAKAS_PRED = scale(df2019$LAKAS_PRED)
# df2019$HO_FORG_RB = scale(df2019$HO_FORG_RB)
# df2019$HO_FORG_OSSZ = scale(df2019$HO_FORG_OSSZ)
# df2019$SZJA = scale(df2019$SZJA)
# df2019$BERUHAZAS = scale(df2019$BERUHAZAS)
n_df2019 = scale((select_if(df2019[,-81], is.numeric)))
sem = "
# measurement model
EU =~ HO_FORG_RB + HO_FORG_OSSZ + FGYHO_SZOLG_SZAM + HO_SZOLG_SZAM + HO_APOLO_SZAM
# regressions
LAKAS_PRED ~ SZJA + BERUHAZAS + MUNKA + ATLAGAR + EU
ATLAGAR ~ SZJA + EU
SZJA ~ MUNKA + BERUHAZAS + EU
# residual correlations
HO_FORG_RB ~~ HGYO_SZAM + HO_APOLO_SZAM + HO_SZOLG_SZAM + FGYHO_SZOLG_SZAM + HO_FORG_OSSZ
HO_FORG_OSSZ ~~ HGYO_SZAM + HO_APOLO_SZAM + HO_SZOLG_SZAM + FGYHO_SZOLG_SZAM
FGYHO_SZOLG_SZAM ~~ HGYO_SZAM + HO_APOLO_SZAM + HO_SZOLG_SZAM
HO_SZOLG_SZAM ~~ HO_APOLO_SZAM + HGYO_SZAM
HO_APOLO_SZAM ~~ HGYO_SZAM
"
fit = sem(sem, data = n_df2019)
summary(fit, standardized = T, fit.measures = T)
## lavaan 0.6.16 ended normally after 113 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 39
##
## Number of observations 175
##
## Model Test User Model:
##
## Test statistic 104.611
## Degrees of freedom 24
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 2263.318
## Degrees of freedom 54
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.964
## Tucker-Lewis Index (TLI) 0.918
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -1150.962
## Loglikelihood unrestricted model (H1) -1098.656
##
## Akaike (AIC) 2379.924
## Bayesian (BIC) 2503.351
## Sample-size adjusted Bayesian (SABIC) 2379.850
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.139
## 90 Percent confidence interval - lower 0.112
## 90 Percent confidence interval - upper 0.166
## P-value H_0: RMSEA <= 0.050 0.000
## P-value H_0: RMSEA >= 0.080 1.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.187
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## EU =~
## HO_FORG_RB 1.000 0.802 0.844
## HO_FORG_OSSZ 0.999 0.005 189.488 0.000 0.801 0.843
## FGYHO_SZOLG_SZ 0.567 0.068 8.383 0.000 0.455 0.486
## HO_SZOLG_SZAM 0.887 0.072 12.384 0.000 0.711 0.716
## HO_APOLO_SZAM 0.780 0.076 10.289 0.000 0.625 0.631
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## LAKAS_PRED ~
## SZJA -0.366 0.096 -3.811 0.000 -0.366 -0.339
## BERUHAZAS 0.058 0.053 1.087 0.277 0.058 0.059
## MUNKA 0.147 0.056 2.630 0.009 0.147 0.151
## ATLAGAR 0.629 0.191 3.299 0.001 0.629 0.628
## EU -0.462 0.286 -1.613 0.107 -0.370 -0.381
## ATLAGAR ~
## SZJA 0.199 0.108 1.847 0.065 0.199 0.184
## EU -0.913 0.174 -5.260 0.000 -0.732 -0.755
## SZJA ~
## MUNKA 0.291 0.048 6.121 0.000 0.291 0.323
## BERUHAZAS 0.242 0.047 5.107 0.000 0.242 0.268
## EU -0.709 0.094 -7.529 0.000 -0.568 -0.631
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .HO_FORG_RB ~~
## HGYO_SZAM -0.183 0.049 -3.733 0.000 -0.183 -0.360
## .HO_APOLO_SZAM 0.141 0.066 2.140 0.032 0.141 0.360
## .HO_SZOLG_SZAM 0.115 0.069 1.660 0.097 0.115 0.326
## .FGYHO_SZOLG_SZ 0.215 0.059 3.659 0.000 0.215 0.514
## .HO_FORG_OSSZ 0.260 0.076 3.420 0.001 0.260 0.995
## .HO_FORG_OSSZ ~~
## HGYO_SZAM -0.184 0.049 -3.744 0.000 -0.184 -0.361
## .HO_APOLO_SZAM 0.141 0.066 2.133 0.033 0.141 0.358
## .HO_SZOLG_SZAM 0.114 0.069 1.650 0.099 0.114 0.322
## .FGYHO_SZOLG_SZ 0.217 0.059 3.694 0.000 0.217 0.518
## .FGYHO_SZOLG_SZAM ~~
## HGYO_SZAM -0.416 0.071 -5.869 0.000 -0.416 -0.510
## .HO_APOLO_SZAM 0.248 0.064 3.859 0.000 0.248 0.395
## .HO_SZOLG_SZAM 0.307 0.065 4.694 0.000 0.307 0.543
## .HO_SZOLG_SZAM ~~
## .HO_APOLO_SZAM 0.353 0.074 4.795 0.000 0.353 0.664
## HGYO_SZAM -0.023 0.057 -0.398 0.690 -0.023 -0.033
## .HO_APOLO_SZAM ~~
## HGYO_SZAM -0.034 0.062 -0.554 0.580 -0.034 -0.045
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .HO_FORG_RB 0.260 0.076 3.424 0.001 0.260 0.288
## .HO_FORG_OSSZ 0.262 0.076 3.446 0.001 0.262 0.290
## .FGYHO_SZOLG_SZ 0.669 0.079 8.435 0.000 0.669 0.764
## .HO_SZOLG_SZAM 0.479 0.081 5.932 0.000 0.479 0.487
## .HO_APOLO_SZAM 0.590 0.083 7.150 0.000 0.590 0.601
## .LAKAS_PRED 0.356 0.045 7.940 0.000 0.356 0.377
## .ATLAGAR 0.207 0.062 3.353 0.001 0.207 0.221
## .SZJA 0.323 0.049 6.584 0.000 0.323 0.398
## HGYO_SZAM 0.994 0.106 9.354 0.000 0.994 1.000
## EU 0.643 0.114 5.648 0.000 1.000 1.000
# summary(fit, fit.measures = T)
n_df = scale((select_if(df2[,-81], is.numeric)))
sem = "
# measurement model
EU =~ HO_FORG_RB + HO_FORG_OSSZ + FGYHO_SZOLG_SZAM + HO_SZOLG_SZAM + HO_APOLO_SZAM
# regressions
LAKAS_PRED ~ SZJA + BERUHAZAS + MUNKA + ATLAGAR + EU
ATLAGAR ~ SZJA + EU
SZJA ~ MUNKA + BERUHAZAS + EU
# residual correlations
HO_FORG_RB ~~ HGYO_SZAM + HO_APOLO_SZAM + HO_SZOLG_SZAM + FGYHO_SZOLG_SZAM + HO_FORG_OSSZ
HO_FORG_OSSZ ~~ HGYO_SZAM + HO_APOLO_SZAM + HO_SZOLG_SZAM + FGYHO_SZOLG_SZAM
FGYHO_SZOLG_SZAM ~~ HGYO_SZAM + HO_APOLO_SZAM + HO_SZOLG_SZAM
HO_SZOLG_SZAM ~~ HO_APOLO_SZAM + HGYO_SZAM
HO_APOLO_SZAM ~~ HGYO_SZAM
"
fit = sem(sem, data = n_df)
summary(fit, fit.measures = T)
## lavaan 0.6.16 ended normally after 78 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 39
##
## Number of observations 1750
##
## Model Test User Model:
##
## Test statistic 645.028
## Degrees of freedom 24
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 16685.051
## Degrees of freedom 54
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.963
## Tucker-Lewis Index (TLI) 0.916
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -14323.769
## Loglikelihood unrestricted model (H1) -14001.255
##
## Akaike (AIC) 28725.538
## Bayesian (BIC) 28938.765
## Sample-size adjusted Bayesian (SABIC) 28814.866
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.122
## 90 Percent confidence interval - lower 0.114
## 90 Percent confidence interval - upper 0.130
## P-value H_0: RMSEA <= 0.050 0.000
## P-value H_0: RMSEA >= 0.080 1.000
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.118
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|)
## EU =~
## HO_FORG_RB 1.000
## HO_FORG_OSSZ 0.931 0.012 78.382 0.000
## FGYHO_SZOLG_SZ 0.508 0.027 18.881 0.000
## HO_SZOLG_SZAM 0.889 0.030 29.689 0.000
## HO_APOLO_SZAM 0.787 0.031 25.349 0.000
##
## Regressions:
## Estimate Std.Err z-value P(>|z|)
## LAKAS_PRED ~
## SZJA -0.214 0.042 -5.036 0.000
## BERUHAZAS 0.034 0.025 1.368 0.171
## MUNKA 0.116 0.031 3.739 0.000
## ATLAGAR 0.620 0.073 8.462 0.000
## EU -0.303 0.099 -3.054 0.002
## ATLAGAR ~
## SZJA 0.472 0.017 27.579 0.000
## EU -0.898 0.055 -16.438 0.000
## SZJA ~
## MUNKA 0.552 0.016 35.603 0.000
## BERUHAZAS 0.306 0.015 19.840 0.000
## EU -0.403 0.025 -16.325 0.000
##
## Covariances:
## Estimate Std.Err z-value P(>|z|)
## .HO_FORG_RB ~~
## HGYO_SZAM -0.307 0.020 -15.665 0.000
## .HO_APOLO_SZAM 0.258 0.025 10.417 0.000
## .HO_SZOLG_SZAM 0.243 0.026 9.441 0.000
## .FGYHO_SZOLG_SZ 0.364 0.023 16.108 0.000
## .HO_FORG_OSSZ 0.442 0.028 15.828 0.000
## .HO_FORG_OSSZ ~~
## HGYO_SZAM -0.323 0.020 -15.855 0.000
## .HO_APOLO_SZAM 0.292 0.025 11.850 0.000
## .HO_SZOLG_SZAM 0.269 0.025 10.563 0.000
## .FGYHO_SZOLG_SZ 0.379 0.023 16.606 0.000
## .FGYHO_SZOLG_SZAM ~~
## HGYO_SZAM -0.523 0.025 -20.808 0.000
## .HO_APOLO_SZAM 0.383 0.024 16.278 0.000
## .HO_SZOLG_SZAM 0.432 0.024 17.933 0.000
## .HO_SZOLG_SZAM ~~
## .HO_APOLO_SZAM 0.462 0.027 17.212 0.000
## HGYO_SZAM -0.143 0.020 -7.050 0.000
## .HO_APOLO_SZAM ~~
## HGYO_SZAM -0.167 0.021 -7.866 0.000
##
## Variances:
## Estimate Std.Err z-value P(>|z|)
## .HO_FORG_RB 0.452 0.029 15.641 0.000
## .HO_FORG_OSSZ 0.514 0.028 18.226 0.000
## .FGYHO_SZOLG_SZ 0.800 0.029 27.679 0.000
## .HO_SZOLG_SZAM 0.603 0.030 20.382 0.000
## .HO_APOLO_SZAM 0.679 0.029 23.170 0.000
## .LAKAS_PRED 0.482 0.017 27.769 0.000
## .ATLAGAR 0.169 0.020 8.522 0.000
## .SZJA 0.223 0.008 26.386 0.000
## HGYO_SZAM 0.999 0.034 29.580 0.000
## EU 0.451 0.034 13.160 0.000
n_df2018 = scale((select_if(df2018[,-81], is.numeric)))
sem = "
# measurement model
EU =~ HO_FORG_RB + HO_FORG_OSSZ + FGYHO_SZOLG_SZAM + HO_SZOLG_SZAM + HO_APOLO_SZAM
# regressions
LAKAS_PRED ~ SZJA + SERTETT + ATLAGAR + EU + BERUHAZAS + MUNKA
ATLAGAR ~ SZJA + EU
SZJA ~ MUNKA + BERUHAZAS + EU
# residual correlations
HO_FORG_RB ~~ HGYO_SZAM + HO_APOLO_SZAM + HO_SZOLG_SZAM + FGYHO_SZOLG_SZAM + HO_FORG_OSSZ
HO_FORG_OSSZ ~~ HGYO_SZAM + HO_APOLO_SZAM + HO_SZOLG_SZAM + FGYHO_SZOLG_SZAM
FGYHO_SZOLG_SZAM ~~ HGYO_SZAM + HO_APOLO_SZAM + HO_SZOLG_SZAM
HO_SZOLG_SZAM ~~ HO_APOLO_SZAM + HGYO_SZAM
HO_APOLO_SZAM ~~ HGYO_SZAM
"
fit = sem(sem, data = n_df2018)
summary(fit, standardized = T, fit.measures = T)
## lavaan 0.6.16 ended normally after 103 iterations
##
## Estimator ML
## Optimization method NLMINB
## Number of model parameters 40
##
## Number of observations 175
##
## Model Test User Model:
##
## Test statistic 91.118
## Degrees of freedom 32
## P-value (Chi-square) 0.000
##
## Model Test Baseline Model:
##
## Test statistic 2201.536
## Degrees of freedom 63
## P-value 0.000
##
## User Model versus Baseline Model:
##
## Comparative Fit Index (CFI) 0.972
## Tucker-Lewis Index (TLI) 0.946
##
## Loglikelihood and Information Criteria:
##
## Loglikelihood user model (H0) -1175.106
## Loglikelihood unrestricted model (H1) -1129.547
##
## Akaike (AIC) 2430.213
## Bayesian (BIC) 2556.804
## Sample-size adjusted Bayesian (SABIC) 2430.137
##
## Root Mean Square Error of Approximation:
##
## RMSEA 0.103
## 90 Percent confidence interval - lower 0.078
## 90 Percent confidence interval - upper 0.128
## P-value H_0: RMSEA <= 0.050 0.000
## P-value H_0: RMSEA >= 0.080 0.938
##
## Standardized Root Mean Square Residual:
##
## SRMR 0.175
##
## Parameter Estimates:
##
## Standard errors Standard
## Information Expected
## Information saturated (h1) model Structured
##
## Latent Variables:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## EU =~
## HO_FORG_RB 1.000 0.816 0.860
## HO_FORG_OSSZ 1.000 0.006 155.799 0.000 0.816 0.860
## FGYHO_SZOLG_SZ 0.555 0.069 8.023 0.000 0.453 0.481
## HO_SZOLG_SZAM 0.851 0.072 11.838 0.000 0.694 0.699
## HO_APOLO_SZAM 0.816 0.074 10.981 0.000 0.666 0.674
##
## Regressions:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## LAKAS_PRED ~
## SZJA -0.241 0.075 -3.229 0.001 -0.241 -0.217
## SERTETT 0.059 0.044 1.346 0.178 0.059 0.058
## ATLAGAR 0.788 0.146 5.399 0.000 0.788 0.755
## EU -0.290 0.207 -1.402 0.161 -0.237 -0.235
## BERUHAZAS 0.035 0.046 0.751 0.452 0.035 0.034
## MUNKA -0.012 0.051 -0.228 0.820 -0.012 -0.012
## ATLAGAR ~
## SZJA 0.228 0.110 2.071 0.038 0.228 0.214
## EU -0.860 0.189 -4.537 0.000 -0.701 -0.726
## SZJA ~
## MUNKA 0.303 0.052 5.872 0.000 0.303 0.333
## BERUHAZAS 0.189 0.051 3.699 0.000 0.189 0.208
## EU -0.652 0.106 -6.144 0.000 -0.532 -0.587
##
## Covariances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .HO_FORG_RB ~~
## HGYO_SZAM -0.198 0.050 -3.968 0.000 -0.198 -0.409
## .HO_APOLO_SZAM 0.116 0.083 1.397 0.162 0.116 0.329
## .HO_SZOLG_SZAM 0.116 0.085 1.359 0.174 0.116 0.338
## .FGYHO_SZOLG_SZ 0.221 0.068 3.232 0.001 0.221 0.553
## .HO_FORG_OSSZ 0.233 0.096 2.411 0.016 0.233 0.992
## .HO_FORG_OSSZ ~~
## HGYO_SZAM -0.199 0.050 -3.994 0.000 -0.199 -0.412
## .HO_APOLO_SZAM 0.115 0.083 1.383 0.167 0.115 0.326
## .HO_SZOLG_SZAM 0.114 0.085 1.335 0.182 0.114 0.332
## .FGYHO_SZOLG_SZ 0.222 0.068 3.249 0.001 0.222 0.556
## .FGYHO_SZOLG_SZAM ~~
## HGYO_SZAM -0.405 0.071 -5.672 0.000 -0.405 -0.492
## .HO_APOLO_SZAM 0.251 0.071 3.552 0.000 0.251 0.417
## .HO_SZOLG_SZAM 0.328 0.074 4.454 0.000 0.328 0.559
## .HO_SZOLG_SZAM ~~
## .HO_APOLO_SZAM 0.329 0.085 3.866 0.000 0.329 0.636
## HGYO_SZAM -0.020 0.059 -0.330 0.742 -0.020 -0.028
## .HO_APOLO_SZAM ~~
## HGYO_SZAM -0.050 0.060 -0.834 0.404 -0.050 -0.069
##
## Variances:
## Estimate Std.Err z-value P(>|z|) Std.lv Std.all
## .HO_FORG_RB 0.235 0.097 2.429 0.015 0.235 0.260
## .HO_FORG_OSSZ 0.234 0.097 2.428 0.015 0.234 0.260
## .FGYHO_SZOLG_SZ 0.682 0.084 8.145 0.000 0.682 0.769
## .HO_SZOLG_SZAM 0.505 0.093 5.414 0.000 0.505 0.511
## .HO_APOLO_SZAM 0.532 0.091 5.823 0.000 0.532 0.545
## .LAKAS_PRED 0.296 0.034 8.689 0.000 0.296 0.291
## .ATLAGAR 0.228 0.072 3.163 0.002 0.228 0.245
## .SZJA 0.383 0.057 6.765 0.000 0.383 0.466
## HGYO_SZAM 0.994 0.106 9.354 0.000 0.994 1.000
## EU 0.666 0.129 5.170 0.000 1.000 1.000
lavaanPlot(model = fit, node_options = list(shape = "box", fontname = "Helvetica"), edge_options = list(color = "grey"), coefs = T)